

import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
	import onnxruntime as ort



class        A__(a_,  unittest.TestCase							):
						"""simple docstring"""






						_A    :				Dict       					=			'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''





						def 			UpperCamelCase__					(    self  ,      _lowercase=0    )     -> List[Any]:
									a_						:						str 							= floats_tensor((1, 3, 128, 128)  ,      rng=random.Random(_lowercase    )    )
									a_						:						int 							= np.random.RandomState(_lowercase    )
									a_						:						Union[str, Any] 							= {
									    """prompt""": """A painting of a squirrel eating a burger""",
									    """image""": image,
									    """generator""": generator,
									    """num_inference_steps""": 3,
									    """strength""": 0.7_5,
									    """guidance_scale""": 7.5,
									    """output_type""": """numpy""",
									}
									return inputs





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									a_						:						Optional[Any] 							= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,      provider="""CPUExecutionProvider"""    )
									pipe.set_progress_bar_config(disable=_lowercase    )

									a_						:						Dict 							= self.get_dummy_inputs()
									a_						:						Dict 							= pipe(**_lowercase    ).images
									a_						:						List[str] 							= image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 128, 128, 3)
									a_						:						List[Any] 							= np.array([0.6_9_6_4_3, 0.5_8_4_8_4, 0.5_0_3_1_4, 0.5_8_7_6_0, 0.5_5_3_6_8, 0.5_9_6_4_3, 0.5_1_5_2_9, 0.4_1_2_1_7, 0.4_9_0_8_7]    )
									assert np.abs(image_slice - expected_slice    ).max() < 1e-1





						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						Optional[int] 							= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,      provider="""CPUExecutionProvider"""    )
									a_						:						int 							= PNDMScheduler.from_config(pipe.scheduler.config  ,      skip_prk_steps=_lowercase    )
									pipe.set_progress_bar_config(disable=_lowercase    )

									a_						:						List[Any] 							= self.get_dummy_inputs()
									a_						:						Optional[int] 							= pipe(**_lowercase    ).images
									a_						:						List[Any] 							= image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									a_						:						Optional[Any] 							= np.array([0.6_1_7_3_7, 0.5_4_6_4_2, 0.5_3_1_8_3, 0.5_4_4_6_5, 0.5_2_7_4_2, 0.6_0_5_2_5, 0.4_9_9_6_9, 0.4_0_6_5_5, 0.4_8_1_5_4]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-1





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_						:						Optional[Any] 							= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,      provider="""CPUExecutionProvider"""    )
									a_						:						Tuple 							= LMSDiscreteScheduler.from_config(pipe.scheduler.config    )
									pipe.set_progress_bar_config(disable=_lowercase    )

									# warmup pass to apply optimizations
									a_						:						Any 							= pipe(**self.get_dummy_inputs()    )

									a_						:						List[str] 							= self.get_dummy_inputs()
									a_						:						List[str] 							= pipe(**_lowercase    ).images
									a_						:						Optional[Any] 							= image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									a_						:						Optional[Any] 							= np.array([0.5_2_7_6_1, 0.5_9_9_7_7, 0.4_9_0_3_3, 0.4_9_6_1_9, 0.5_4_2_8_2, 0.5_0_3_1_1, 0.4_7_6_0_0, 0.4_0_9_1_8, 0.4_5_2_0_3]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-1





						def 			UpperCamelCase__					(    self    )     -> Union[str, Any]:
									a_						:						List[Any] 							= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,      provider="""CPUExecutionProvider"""    )
									a_						:						Union[str, Any] 							= EulerDiscreteScheduler.from_config(pipe.scheduler.config    )
									pipe.set_progress_bar_config(disable=_lowercase    )

									a_						:						Optional[int] 							= self.get_dummy_inputs()
									a_						:						Dict 							= pipe(**_lowercase    ).images
									a_						:						Optional[Any] 							= image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									a_						:						Optional[int] 							= np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-1





						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									a_						:						int 							= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,      provider="""CPUExecutionProvider"""    )
									a_						:						Tuple 							= EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config    )
									pipe.set_progress_bar_config(disable=_lowercase    )

									a_						:						Any 							= self.get_dummy_inputs()
									a_						:						List[Any] 							= pipe(**_lowercase    ).images
									a_						:						List[str] 							= image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									a_						:						Any 							= np.array([0.5_2_9_1_1, 0.6_0_0_0_4, 0.4_9_2_2_9, 0.4_9_8_0_5, 0.5_4_5_0_2, 0.5_0_6_8_0, 0.4_7_7_7_7, 0.4_1_0_2_8, 0.4_5_3_0_4]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-1





						def 			UpperCamelCase__					(    self    )     -> int:
									a_						:						str 							= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,      provider="""CPUExecutionProvider"""    )
									a_						:						Union[str, Any] 							= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config    )
									pipe.set_progress_bar_config(disable=_lowercase    )

									a_						:						Dict 							= self.get_dummy_inputs()
									a_						:						Optional[Any] 							= pipe(**_lowercase    ).images
									a_						:						Dict 							= image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									a_						:						int 							= np.array([0.6_5_3_3_1, 0.5_8_2_7_7, 0.4_8_2_0_4, 0.5_6_0_5_9, 0.5_3_6_6_5, 0.5_6_2_3_5, 0.5_0_9_6_9, 0.4_0_0_0_9, 0.4_6_5_5_2]    )

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 1e-1







@nightly
@require_onnxruntime
@require_torch_gpu
class        A__(unittest.TestCase							):
						"""simple docstring"""






						@property
						def 			UpperCamelCase__					(    self    )     -> Tuple:
									return (
									    "CUDAExecutionProvider",
									    {
									        "gpu_mem_limit": "15000000000",  # 15GB
									        "arena_extend_strategy": "kSameAsRequested",
									    },
									)





						@property
						def 			UpperCamelCase__					(    self    )     -> List[str]:
									a_						:						Optional[int] 							= ort.SessionOptions()
									a_						:						Optional[int] 							= False
									return options





						def 			UpperCamelCase__					(    self    )     -> Optional[int]:
									a_						:						List[Any] 							= load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/img2img/sketch-mountains-input.jpg"""    )
									a_						:						List[str] 							= init_image.resize((768, 512)    )
									# using the PNDM scheduler by default
									a_						:						Tuple 							= OnnxStableDiffusionImgaImgPipeline.from_pretrained(
									    """CompVis/stable-diffusion-v1-4"""  ,      revision="""onnx"""  ,      safety_checker=_lowercase  ,      feature_extractor=_lowercase  ,      provider=self.gpu_provider  ,      sess_options=self.gpu_options  ,      )
									pipe.set_progress_bar_config(disable=_lowercase    )

									a_						:						Optional[Any] 							= """A fantasy landscape, trending on artstation"""

									a_						:						Tuple 							= np.random.RandomState(0    )
									a_						:						List[str] 							= pipe(
									    prompt=_lowercase  ,      image=_lowercase  ,      strength=0.7_5  ,      guidance_scale=7.5  ,      num_inference_steps=10  ,      generator=_lowercase  ,      output_type="""np"""  ,      )
									a_						:						List[Any] 							= output.images
									a_						:						Any 							= images[0, 255:258, 383:386, -1]

									assert images.shape == (1, 512, 768, 3)
									a_						:						str 							= np.array([0.4_9_0_9, 0.5_0_5_9, 0.5_3_7_2, 0.4_6_2_3, 0.4_8_7_6, 0.5_0_4_9, 0.4_8_2_0, 0.4_9_5_6, 0.5_0_1_9]    )
									# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 2e-2





						def 			UpperCamelCase__					(    self    )     -> Optional[Any]:
									a_						:						Any 							= load_image(
									    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
									    """/img2img/sketch-mountains-input.jpg"""    )
									a_						:						Optional[Any] 							= init_image.resize((768, 512)    )
									a_						:						List[str] 							= LMSDiscreteScheduler.from_pretrained(
									    """runwayml/stable-diffusion-v1-5"""  ,      subfolder="""scheduler"""  ,      revision="""onnx"""    )
									a_						:						int 							= OnnxStableDiffusionImgaImgPipeline.from_pretrained(
									    """runwayml/stable-diffusion-v1-5"""  ,      revision="""onnx"""  ,      scheduler=_lowercase  ,      safety_checker=_lowercase  ,      feature_extractor=_lowercase  ,      provider=self.gpu_provider  ,      sess_options=self.gpu_options  ,      )
									pipe.set_progress_bar_config(disable=_lowercase    )

									a_						:						Tuple 							= """A fantasy landscape, trending on artstation"""

									a_						:						Optional[int] 							= np.random.RandomState(0    )
									a_						:						str 							= pipe(
									    prompt=_lowercase  ,      image=_lowercase  ,      strength=0.7_5  ,      guidance_scale=7.5  ,      num_inference_steps=20  ,      generator=_lowercase  ,      output_type="""np"""  ,      )
									a_						:						Tuple 							= output.images
									a_						:						int 							= images[0, 255:258, 383:386, -1]

									assert images.shape == (1, 512, 768, 3)
									a_						:						str 							= np.array([0.8_0_4_3, 0.9_2_6, 0.9_5_8_1, 0.8_1_1_9, 0.8_9_5_4, 0.9_1_3, 0.7_2_0_9, 0.7_4_6_3, 0.7_4_3_1]    )
									# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

									assert np.abs(image_slice.flatten() - expected_slice    ).max() < 2e-2





from __future__ import annotations


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			a_						:						List[str] 							= 0
			a_						:						str 							= len(a__) - 1

			while i < j:
						if nums[i] + nums[j] == target:
									return [i, j]
						elif nums[i] + nums[j] < target:
									a_						:						List[Any] 							= i + 1
						else:
									a_						:						Optional[Any] 							= j - 1

			return []


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	print(F"""{two_pointer([2, 7, 11, 15], 9) = }""")


